104results about "Forest fire alarms" patented technology

The invention provides a lightning trajectory data fusion detection based single-tree accurate early warning system for lightning fire, and relates to the technical field of meteorological disaster monitoring and early warning and forest lightning fire prevention and control. The lightning trajectory data fusion detection based single-tree accurate early warning system comprises an electromagnetic radiation receiving module which adopts an antenna array composed of at least four directional antennas to receive electromagnetic radiation signals generated by lightning, the antenna array is connected with a signal conditioning circuit, and the signal conditioning circuit is connected with the electromagnetic radiation receiving module. The signal conditioning circuit amplifies the received weak electromagnetic signal by 100-1000 times, processes the weak electromagnetic signal with a filtering bandwidth of 10 kHz to 1 MHz, and transmits the weak electromagnetic signal to a data acquisition card with a sampling frequency of 5-20 MS / s and a sampling precision of 12-16 bits. By integrating lightning electromagnetic radiation continuous sampling, infrared, visible light and ultraviolet multispectral tracking shooting and atmospheric electric field early warning multi-modal data, a lightning track is accurately detected, and by combining forest environment information, accurate early warning of a single tree where a lightning fire may occur is achieved.

The invention discloses a power grid facility safety-oriented forest fire spreading early warning method and system, and particularly relates to the field of cloud edge collaboration and danger prediction.The method comprises the steps of arranging a plurality of monitoring nodes in a power grid protection area, collecting monitoring data of temperature, humidity, wind speed, smoke concentration and spectral radiation, and performing early warning on forest fire spreading; matching the monitoring data of each monitoring node with the corresponding acquisition time, and constructing a multi-node environment data set according to a matching result; according to the method, a time rhythm index and multi-node difference identification mechanism is established under a cloud edge collaborative architecture, and an integrated danger prediction process of data synchronization, difference correction and trend solution is realized, so that the problems of virtual height early warning and response lag caused by misjudgment of node data asynchronization and cloud fusion in existing forest fire monitoring are solved.

The invention discloses a forest fire rapid identification and accurate fire point positioning method based on air-ground cooperation. Comprising the following steps: a forest fire panoramic monitoring and positioning method based on a double-axis holder, a wildfire target intelligent identification algorithm based on multi-modal image fusion analysis, and a low-power-consumption air-ground cooperative communication network oriented to forest fire monitoring. The invention relates to an unmanned aerial vehicle-based forest wildfire target maneuvering inspection and positioning method and an air-ground fusion wildfire target accurate positioning method. According to the invention, 360-degree panoramic image acquisition is realized by using the double-shaft holder, and meanwhile, a fire source position self-adaptive adjustment mechanism is provided; multi-modal image fusion analysis is adopted in the image processing layer to resist interference, blind areas are compensated, and the fire source recognition accuracy is improved; a low-power-consumption air-ground cooperative communication network is constructed, so that the cost is reduced, and flight is continued; and the monitoring limitation is broken through by means of flexible inspection of the unmanned aerial vehicle, a closed-loop system of air-ground cooperation-information complementation-intelligent verification is formed, and the monitoring stability in a complex scene is remarkably enhanced.

The invention discloses a forest fire unmanned aerial vehicle video monitoring system and method. The system comprises a data acquisition unit arranged on an unmanned aerial vehicle and responsible for acquiring multi-source monitoring data and spatial attitude data of a target area; the data processing unit is arranged on a ground station and is used for receiving and processing the data and generating fused situation data containing the position and situation of the fire behavior through a fire behavior recognition algorithm; the dynamic path planning unit is used for automatically generating a cruising or path tracking instruction according to the fused situation data, the geographic information and the state of the unmanned aerial vehicle; and the command decision-making unit is arranged in a ground command center and is responsible for visualized display, spatial analysis and emergency command of the fire behavior and issuing a path instruction to the unmanned aerial vehicle for execution so as to realize a monitoring and disposal closed loop. According to the invention, multi-source data efficient processing, fire accurate identification, dynamic path planning and emergency command efficient linkage can be realized, the monitoring coverage range and the response speed are improved, the labor cost is reduced, and the fire loss is reduced.

The invention relates to the technical field of forest fire alarm monitoring, in particular to a forest fire early warning method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring image data and atmospheric spectrum data of a forest area; the atmospheric spectral data is spectral data which is acquired through a Fourier transform infrared spectroscopy FTIR technology and contains a COcharacteristic absorption wave band; identifying visual features of flames and / or smoke from the collected image data through a deep learning model; carrying out baseline calibration and characteristic peak identification processing on the atmospheric spectrum data, and calculating the carbon dioxide concentration; determining a fire danger grade according to the visual features and the carbon dioxide concentration; and generating early warning information based on the fire danger level. Compared with the prior art, the FTIR technology is introduced into the field of forest fire early warning, so that the monitoring precision and the response rate are greatly improved. Meanwhile, early warning evaluation is carried out by fusing two dimensions of optical images and carbon detection instead of being based on a single index, so that the monitoring precision is improved, and the false alarm rate is reduced.

The invention discloses a forest fire early recognition and early warning system and method based on multi-source sensing and artificial intelligence, and belongs to the technical field of forest fire intelligent monitoring. The system comprises a gridding multi-source sensing deployment module, a multi-modal time sequence data collaborative acquisition module, a cross-modal feature coupling and state diagnosis module, a multi-source heterogeneous feature adaptive fusion and fire behavior identification module and an intelligent grading early warning and dynamic resource collaborative scheduling module. The method comprises the steps of heterogeneous grid division and sensing deployment, multi-modal time series data collaborative acquisition, cross-modal feature coupling analysis, multi-channel attention fusion recognition, and graded early warning and dynamic scheduling. Through multi-source data collaborative sensing, cross-modal feature coupling analysis, adaptive fusion recognition and intelligent scheduling response, accurate and rapid intelligent early warning of the early stage of the forest fire is realized, and the intelligent level and emergency response efficiency of forest fire prevention and control are significantly improved.

Systems and methods for utility infrastructure condition monitoring, detection, and response are disclosed. One exemplary system includes a sensor package and a monitoring and control module. The sensor package includes a plurality of sensors such as, for example, an image sensor, a video sensor, and a LiDAR sensor. The sensors may each be configured to capture data indicative of one or more conditions (e.g., an environmental condition, a structural condition, etc.) in the vicinity of the utility infrastructure. The monitoring and control includes a detection module and an alert module. The detection module is configured to receive data captured by each sensor and, based on the captured data, determine one or more conditions in the vicinity of the utility infrastructure. The detection module may be configured to then, based on the determined conditions, provide an alert for the condition using the alert module.

The invention discloses a forest fire danger early warning system and method based on meteorological and vegetation data analysis, and relates to the technical field of fire risk prediction and control. According to the forest fire danger early warning system and method based on meteorological and vegetation data analysis, fine grid division is carried out on a target forest area through a high-resolution DEM and a multispectral or hyperspectral image, and vegetation type identification is carried out on each monitoring subunit; calculating a wet-dry cycle index WDI, a monsoon effect index MFI and a dry thunderstorm risk factor DRF by combining hourly wind speed, wind direction, relative humidity, precipitation and vapor pressure difference meteorological data; through extracting canopy height, canopy density, under-forest shrub and grass layer thickness and high VOC tree species coverage rate, fuel continuity coefficient FCI and volatile matter flammability index VEI are calculated, and accurate quantification of continuity and flammability of vertical ladder fuel and horizontal canopy is realized. And a comprehensive fire danger index FFI is constructed and subjected to hierarchical management after judgment, so that the dynamism, the hierarchical property and the pertinence of fire danger early warning are realized, and the forest fire danger prevention and control precision and the patrol resource utilization efficiency are improved.

The invention discloses a forest fire risk dynamic assessment and early warning system based on multi-source data fusion and artificial intelligence, and belongs to the technical field of forest fire prevention and disaster early warning. The system aims at solving the technical problems that in an existing forest fire monitoring technology, evaluation lags behind, data is single, and risk prospective prediction cannot be achieved. According to the technical scheme, satellite microwave remote sensing, optical remote sensing and near real-time meteorological and topographic data are integrated through a multi-source data acquisition module, and are fused and processed through a feature extraction module to generate a fire danger influence factor feature set containing the water content, the load capacity, the meteorological conditions and the topographic features of combustible materials; a dynamic risk distribution diagram in a future preset time period is output through a machine learning model of the dynamic risk assessment module, and finally automatic early warning is achieved through the early warning and decision making module. The system is mainly used for realizing scientific and accurate assessment and early warning of forest fire risks, and provides technical support for forest fire prevention decision making.

The invention belongs to the field of satellite remote sensing fire monitoring, and discloses a satellite remote sensing forest fire initial fire point monitoring method, which comprises the following steps: acquiring satellite data as input data; reading the zenith angle of the sun in the satellite data, and judging the time period as a day time period, a night time period and a morning and night junction time period; the solar zenith angle, the sensor zenith angle and the like need to be read in daytime period data so as to filter solar flares; carrying out simple cloud detection on data in all time periods by utilizing 11-micron channel brightness temperature and 11-4-micron channel brightness temperature, expanding an adaptive window by taking a suspected initial fire point pixel as a center, and calculating 4-micron channel brightness temperature difference, 11-micron channel brightness temperature difference and 4-11-micron channel brightness temperature difference of a background pixel by utilizing a time sequence method; and counting the mean value and the standard deviation of the background pixels, and confirming and extracting a forecast initial fire point by using a normal distribution starting point algorithm. According to the method, the timeliness and accuracy of early warning of the fire disaster are improved, and important technical support is provided for forest fire prevention decision making.

The present application belongs to the technical field of forest fire research, and specifically discloses a forest fire simulation monitoring method and system. The method divides multiple forest fire simulation areas, determines the area where the ignition point is located as a target area; uses a UAV to carry a Fourier infrared spectrometer to collect spectral images in the target area; compares the spectral images with the spectral bands generated by the combustion of existing combustion products to determine the types of combustion products in the target area; based on the spectral images in the target area and the corresponding combustion product types, a flame spectral radiation model is constructed; multiple forest fire danger levels and the corresponding flame spectral radiation ranges of each forest fire danger level are set in advance, and the flame spectral radiation parameters output by the flame spectral radiation model are compared with all flame spectral radiation ranges to determine the forest fire danger level of the target area. By using the flame spectral radiation model, the present application realizes accurate detection and identification of forest fires.

The invention relates to the field of photoelectric detection and remote sensing monitoring, and provides an intelligent forest fire identification method. The method comprises the steps that image data of a monitoring area are acquired in real time through a multispectral data acquisition module, and the image data comprise thermal imaging data and visible light data; an embedded processing unit is used for processing image data in real time, and automatic identification and positioning of fire points are achieved through a multispectral data fusion algorithm; the power output of a power supply system is dynamically adjusted by adopting an intelligent energy management strategy to support all-weather operation of equipment, and the power supply system comprises a renewable energy power generation unit and an energy storage unit; based on the topographic data, adjusting the spatial attitude of the intelligent forest fire identification equipment through an adjustable supporting structure; after a fire point is identified, alarm information is transmitted to a monitoring center through a communication module, and an emergency response mechanism is triggered. According to the application, high accuracy and all-weather stable operation of forest fire monitoring are realized through multispectral collaborative identification and intelligent energy management.

The invention relates to the technical field of intelligent monitoring, and discloses a video monitoring and early warning system for forest fire prevention, and the system comprises a video monitoring data collection module which is used for obtaining video image data, combustible moisture content data and wind speed and wind direction data; the smoke and fire identification and alarm module is used for identifying smoke and fire and generating video alarm information; the video alarm rechecking module is used for receiving the video alarm information, performing fusion judgment and outputting a decision signal; the video fire deduction module is used for deducing and generating video fire situation assessment in response to the decision signal; the plan scheduling decision module is used for generating an emergency disposal plan and decomposing the emergency disposal plan into task instructions for distribution; and the video early warning display module is used for performing fusion display and interaction on the command interface. According to the invention, an automatic closed loop from intelligent identification, re-check research and judgment and situation prediction to command and dispatch is constructed, and the early warning accuracy and emergency response efficiency of forest fire prevention are improved.

The invention relates to the technical field of forest fire prevention, in particular to a fire early warning device for forest fire prevention. The fixed shell is fixedly connected with the base; the warning mechanism is connected with the fixed shell in a sliding manner; the fixing mechanism is arranged between the fixing shell and the base and is connected with the warning mechanism; wherein the fixing mechanism comprises a control assembly, and the control assembly is fixedly connected to the inner side of the fixing shell; the positioning assembly is connected with the base, and the positioning assembly is connected with the control assembly; the lifting assembly is arranged between the control assembly and the warning mechanism, by arranging the fixing mechanism, multiple fixing of equipment can be completed through the positioning assembly, then the firmness of the equipment in soil is guaranteed, the stability of the device is improved, the equipment can be collected and released, and the practicability is high. And therefore, the convenience of the equipment in transportation is improved.

In accordance with various aspects of the present disclosure, methods and systems for fire detection are disclosed. In some embodiments, a method for fire detection includes: acquiring data related to a monitored area, wherein the data comprises image data related to the monitored area; determining, based on the acquired data, whether a first mode or a second mode is to be executed for fire detection, wherein the first mode comprises a first smoke detection, and wherein the second mode comprises a first flame detection; and executing, by a hardware processor, at least one of the first mode or the second mode based on the determination.

A flame detector including: a plurality of detectors; a plurality of filters; a plurality of non-imaging optical concentrators having an entrance arranged to receive light incident on the flame detector and an exit arranged to deliver the light to a coupled detector. A first of the plurality of filters transmits light of a first wavelength range to a first of the detectors, and a second of the plurality of filters transmits light of a second wavelength range to a second of the detectors, wherein the second wavelength range is different from the first wavelength range.

The invention discloses a black-night power transmission forest fire monitoring dual-light fusion method and system based on SAM semantic enhancement. The method comprises the steps that visible light and infrared images around a power transmission line in a black-night environment are collected and preprocessed respectively; extracting exclusive semantics of the night mountain fire from the preprocessed visible light and infrared images based on an SAM model, and performing semantic feature enhancement to obtain a corresponding feature map; performing hierarchical feature extraction and dynamic fusion according to the feature map to obtain a final fusion feature; constructing a main network, and training the main network by taking the quality of the final fusion feature and the forest fire recognition precision as targets; constructing a sub-network for forest fire recognition, and training the sub-network by taking the fusion feature between the sub-network and the main network and the minimum distance of the forest fire probability graph as a target; and outputting a forest fire probability graph according to the acquired image by using the trained sub-network to carry out forest fire early warning. The method can achieve the precise recognition and positioning of the forest fire around the power transmission line.

The invention describes a method for fighting a forest fire comprising the process steps of localizing a fire source and generating and emitting a gas vortex with a gas vortex generator of a forest fire fighting device, wherein the gas vortex is emitted onto the fire source, and wherein the gas vortex contains a gas mixture, wherein the gas mixture contains more than 5 vol% carbon dioxide, as well as a device for fighting a forest fire.

The application belongs to the field of fire prevention monitoring, and particularly relates to an all-weather forest fire prevention intelligent monitoring system suitable for complex environments, which constructs a dynamic monitoring path based on thermal radiation gradient analysis through a collection module, and identifies abnormal areas in real time; an image quality is improved by using a selection enhancement module to adaptively call an enhanced filtering model according to meteorological data; a dual recognition module is used to fuse thermal imaging and visible light features, and a fire situation is accurately distinguished through a multi-level filtering and bidirectional verification mechanism; finally, a fire situation positioning, situation assessment and graded early warning are realized through an early warning module; the application solves the problems of low early fire situation recognition sensitivity and high false alarm rate in complex environments, and realizes all-weather and high-precision forest fire situation monitoring and early warning.

The present invention relates to a fire detection system comprising a plurality of detection devices (2) forming a network covering a monitored area, and a computer platform (3), characterized in that each detection device (2) is configured to acquire an infrared image of the monitored area (1), to detect a local increase in infrared radiation, and to transmit a digital alarm signal to the computer platform (3) if a local increase in infrared radiation is detected.

The invention discloses an electric power forest fire early warning method and device based on a quantum measurement technology. The method comprises the steps that multiple pieces of sensing data of a target position in a power line are acquired through a quantum sensor, and the multiple pieces of sensing data comprise temperature data, gas concentration data and electric field intensity data; based on the multiple pieces of sensing data, determining a forest fire risk level of the target position, the forest fire risk level including a low risk, a medium risk, a high risk or an extremely high risk; and performing early warning operation on the target position based on the forest fire risk level. According to the invention, the technical problems of low monitoring precision, slow response, easy interference and unsafe data transmission in a traditional electric power forest fire early warning method are solved.

The invention relates to a forest fire monitoring method, device and equipment and a readable storage medium. The method comprises the following steps: acquiring fire monitoring data of a target area collected by a monitoring device; the fire monitoring data comprises environment image data and corresponding device pose data; determining a first fire confidence coefficient of the target area based on the environment image data, and determining a fire state based on the first fire confidence coefficient; under the condition that the fire behavior state is that the fire behavior exists, determining the fire point position where the fire behavior exists based on the device pose data; and the monitoring visual angle of the monitoring device is adjusted, so that the fire behavior at the fire point position can be monitored in all directions. By adopting the method, forest fire monitoring can be accurately carried out.

The invention discloses an intelligent forest fire extinguishing system based on unmanned aerial vehicles, which comprises a cloud control platform, a reconnaissance unmanned aerial vehicle cluster, a fire extinguishing unmanned aerial vehicle cluster, a distributed charging supply station and a GIS geographic information database, and is characterized in that the cloud control platform is connected with the reconnaissance unmanned aerial vehicle cluster, the fire extinguishing unmanned aerial vehicle cluster, the distributed charging supply station and the GIS geographic information database; the reconnaissance unmanned aerial vehicle cluster is composed of multiple groups of reconnaissance unmanned aerial vehicles, each group of reconnaissance unmanned aerial vehicles comprises a multi-sensor assembly, the multi-sensor assembly comprises a smoke sensor, and a detection cover for accommodating the smoke sensor is mounted at the bottom of each reconnaissance unmanned aerial vehicle. Through multiple effects of fan blowback, conical dynamic deformation of the filter screen, rotary torsion deformation and elastic springing, caked filtered matters in a humid environment can be efficiently shaken off, impurities on the surface of the filter screen can be completely removed, the unmanned aerial vehicle filter screen does not need to be manually cleaned one by one, the labor amount of workers is reduced, the problem that manual cleaning is not thorough is avoided, and the working efficiency is improved. And the system operation and maintenance efficiency is improved.

The invention relates to the technical field of forest fire monitoring, in particular to a live wire propulsion real-time monitoring system and method based on an equipment cluster disconnection state. Comprising a plurality of one-time fire scene monitoring beacons distributed and deployed in a monitoring area, and a cloud monitoring platform in communication connection with the beacons, the one-time fire scene monitoring beacon is configured to send a heartbeat data packet to the cloud monitoring platform according to a preset period; according to the invention, through a unique thermal failure sequential control hardware architecture and a cloud three-level progressive cross validation algorithm, equipment disconnection which is considered as a fault in traditional monitoring is subversively converted into a definite fire arriving signal, so that the problems of monitoring blind areas and high protection cost caused by high-temperature damage of equipment in the prior art are effectively solved; and low-cost and high-density wide area network gridding deployment is realized.

The invention discloses a forest smoke and fire video identification and positioning method based on deep learning and multiple features. The method comprises the following steps: S1, acquiring a monitoring video of a target forest area through an unmanned aerial vehicle; s2, selecting a key frame; s3, extracting features of multiple dimensions in the key frame through a feature extraction module, and fusing the features of the multiple dimensions to generate a multi-dimensional fusion feature; s4, inputting the multi-dimensional fusion features into a trained classification model, and outputting a corresponding smoke and fire recognition result; s5, when the smoke and fire identification result is that a fire occurs, inputting the key frame into the target identification model, and outputting a corresponding smoke and fire target detection frame; and S6, on the basis of the real-time position of the unmanned aerial vehicle, the fire occurrence position in the target forest area is calculated by combining the position of the smoke and fire target detection frame in the key frame. When a fire occurs, the fire occurrence position in the target forest area is calculated based on the real-time position of the unmanned aerial vehicle in combination with the position of the smoke and fire target detection frame in the key frame.

A service inputs wind data and utility data corresponding to a utility component into a first machine learning model to determine an outage risk prediction representing a probability that the utility component will have an outage. The service determines a probability of ignition at a vicinity of the utility component, and determines a set of wildfire impact measurements by simulating a wildfire in the vicinity of the utility component. The service inputs the outage risk prediction, the probability of ignition, and the set of wildfire impact measurements into a second machine learning model, and receives as output from the second machine learning model, a catastrophic wildfire risk score corresponding to the utility component. The service outputs a graphical representation on a dashboard representing the catastrophic wildfire risk score.

The application discloses a kind of based on SAM semantic enhancement's black night power transmission mountain fire monitoring double light fusion method and system, method includes: collecting the visible light and infrared image in the periphery of power transmission line under black night environment and respectively pre-processing;Based on SAM model, extract the black night mountain fire special semantic from the visible light and infrared image after pre-processing, and obtain the corresponding feature map by semantic feature enhancement;According to feature map, hierarchical feature extraction and dynamic fusion are carried out, and the final fusion feature is obtained;The main network is constructed, and the quality of final fusion feature and mountain fire recognition precision are improved as the goal to train the main network;Sub-network for mountain fire recognition is constructed, and the fusion feature and mountain fire probability map distance between sub-network and main network are minimum as the goal to train the sub-network;Mountain fire early warning is carried out using the trained sub-network according to the collected image output mountain fire probability map.The application can realize the accurate identification and positioning of mountain fire in the periphery of power transmission line.

The present application relates to the technical field of computer vision and image processing, and particularly relates to a forest fire monitoring method based on helicopter aerial photography. The method comprises the following steps: collecting fire image data of a forest fire site by a helicopter, and performing image frame registration in time and space to obtain a time-series geographic image data set; comparing and analyzing the pixel position change of smoke texture between adjacent image frames in the time-series geographic image data set, calculating the moving direction and speed of pixels in a unit time, and thus constructing an instantaneous smoke speed vector matrix; performing discrete vorticity scalar field conversion on the instantaneous smoke speed vector matrix to obtain a vorticity scalar field; performing vorticity field construction and vortex core positioning on the vorticity scalar field to obtain dominant vortex core pixel coordinates. The present application realizes accurate positioning of a fire source by computer vision and image processing technology, provides a decision basis for accurate deployment of fire extinguishing resources, and significantly improves fire extinguishing efficiency and forest fire prevention and control capability.

The invention provides a forest fire prediction method and device based on fire point and meteorological data fusion and a medium, and belongs to the technical field of meteorological monitoring and disaster early warning. Fire point acquisition date and time and meteorological effective time are normalized to hour precision, and format conversion and longitude and latitude radian are completed; taking the fire point space-time coordinates as query points, constructing a meteorological candidate set in a dynamic time window, and obtaining neighbor grid points by adopting spherical distance K neighbor search; carrying out weighted averaging on the meteorological variables based on inverse distance space-time weights which simultaneously consider a space spherical distance and a time difference to obtain an optimal weighted meteorological characteristic value of the fire point; accordingly, a fire point meteorological element time sequence is constructed, negative samples are sampled from the fire-point-free report grid points according to a preset negative sample proportion to form a balanced training data set, and large-scale fusion efficiency is improved by combining GPU parallel and video memory management.